Archive for category simulator
the final version of your article Pedestrians’ road crossing decisions and body parts’ movements is now available online, containing full bibliographic details.
- Road-crossing simulator synched with a 3D motion capturing system was built
- Time pressure and longer wait times cause riskier crossing decisions
- Pedestrians adjusted posture, crossing speed and timing of crossing to the risk taken
- Body parts’ movement prior to the crossing can be divided into four increments
In this study we examined pedestrians’ crossing decision, body parts’ movement and full body movement, just before and during road crossing in a simulated setup. To accomplish this, a novel experimental setup for analyzing pedestrians’ crossing behavior and motion was developed where the simulated display was synchronized with a 3D motion capturing system. Twenty participants, divided into control and an experimental time pressure group, observed sixteen short (less than 30 seconds) and long road (70 seconds or more) crossing scenarios with varying crossing opportunities. Based on the crossing opportunities they were asked to cross a 3.6 m wide one-lane one way urban road. It was found that the crossing initiation process consists of four incremental movements of body parts: the head and the shoulder first; the hip, wrist and elbow second; the knee as a separate joint, and finally the ankle. Results showed that pedestrians’ decision to cross and body parts movement are influenced by time pressure and wait time for a safe crossing opportunity. Specifically, pedestrians prepare their body parts earlier, initiate their crossing earlier, and adjust their speed to compensate for the risk taken in less safe or non-safe crossing opportunities. Within the control group, women tended to be more risk avoiding than men, however those differences disappeared in the time pressure group. Most importantly, the findings provide initial evidence that this novel simulation configuration can be used to gain precise knowledge of pedestrians’ decision-making and movement processes.
What did we learn about pedestrians crossing movement?
Pedestrians change their strategy as a function of internal and external reasons:
- Take higher risk when crossing opportunities are sparse or when they are under time pressure
- Prepare their movement in advance by adjusting body position
- Change the timing of crossing as a function of perceived risk
- Adjust their crossing speed to the perceived risk
Kalantarov, S. , Riemer, R., Oron-Gilad, T. (in press). Pedestrians’ road crossing decisions and body parts’ movements. Transportation Research Part F: Psychology and Behaviour.
Yisrael Parmet, Lee Shoham and Tal Oron-Gilad
Presentation at the ICTTP 2016.
link to presentation: How full vehicle automation affects…
DESCRIPTION: The purpose of this study was to examine the effects of full vehicle automation on performance and behavior, specifically the transition from a fully automated mode to manual driving, under the influence of alcohol and without it. Previous studies have revealed a deterioration in driving performance while transitioning from an automated mode to manual driving and further suggested that automated driving may result in a degraded situation awareness. It was therefore hypothesized that the performance of secondary driving related tasks would deteriorate during the automated phase, while performance of secondary non-driving related tasks would improve, in comparison to manual driving. It was further hypothesized that the transition from automated to manual driving would damage driving performance and that alcohol, while affecting performance of all driving conditions, would affect the manual phase following the automated phase to a greater extent. Method. A fixed base driving simulator was used. The design contained a first manual phase, an automated phase and another manual phase, under the influence of BAC 0.05% alcohol and without it. The study involved 16 participants. Two type of secondary tasks were introduced to the participants, driving and non-driving related tasks and the precision (% of success) and response time (RT) were measured. Driving quality indices such as speed and lane position were measures along the drive as well. Results. In the nondriving related secondary task we found significant differences in the response time only, the response time under the placebo condition were on average 15% higher than the response time under the alcohol condition. In the driving related secondary task we found significant difference in both measures, the participants on average were 5% more accurate and 13% faster while they drove manually. The results of the driving quality indices indicate a deterioration in precision of driving related secondary tasks, and a decrease in driving velocity after an automated phase, the latter being moderated by alcohol, which causes an increase in driving velocity. Conclusion. As hypothesized the performance of secondary driving related tasks deteriorated during the automated phase but contrary to our hypothesis, the automation had no influence on the performance of the non-driving secondary task. Opposing to our hypothesis, we found no evidence that alcohol deteriorates the drivers’ performance in the two types of secondary tasks. The last results might be due to the low level of alcohol that was used in the experiment. As expected we found that driving quality decreases after automated phase and while performing secondary tasks.
This is our most recent publication, accepted for publication in Safety Science.
Please cite this article in press as: Tapiro, H., et al. Cell phone conversations and child pedestrian’s crossing behavior; a simulator study. Safety Sci. (2016), http://dx.doi.org/10.1016/j.ssci.2016.05.013
Cell phone conversations and child pedestrian’s crossing behavior; a simulator study
Hagai Tapiro, Yisrael Parmet and Tal Oron-Gilad
Child pedestrians are highly represented in fatal and severe road crashes and differ in their crossing behavior from adults. Although many children carry cell phones, the effect that cell phone conversations have on children’s crossing behavior has not been thoroughly examined. A comparison of children and adult pedestrians’ crossing behavior while engaged in cell phone conversations was conducted. In a semi-immersive virtual environment simulating a typical city, 14 adults and 38 children (11 children aged 7-8; 18 aged 9-10 and 9 aged 11-13), experienced road crossing related traffic-scene scenarios. They were requested to press a response button whenever they felt it was safe to cross. Eye movements were tracked. Results have shown that all age groups’ crossing behaviors were affected by cell phone conversations. When busy with more cognitively demanding conversation types, participants were slower to react to a crossing opportunity, chose smaller crossing gaps, and allocated less visual attention to the peripheral regions of the scene. The ability to make better crossing decisions improved with age, but no interaction with cell phone conversation type was found. The most prominent improvement was shown in ‘safety gap’; each age group maintained a longer gap than its predecessor younger age group. In accordance to the current study, it is safe to say that cell phone conversations can hinder child and adult pedestrians’ safety. Thereby, it is important to take those findings in account when aiming to train young pedestrians for road-safety and increase public awareness.
Interested in seeing an interactive visualization app of our data?https://eyemove.shinyapps.io/cell-phone/
Here we report upon results of a validation study conducted on our unique pedestrian simulator.
The simulator validation study confirms the simulator’s ability to correctly simulate the real road environment, and strengthens the reliability as a source for statistical Inference. The goal of this work was to investigate whether the Dome simulator successfully simulates typical pedestrian environment in a manner that will elicit people to act in the same manner as they would in the real world crossing situations. Data analysis shows that the simulator delivers more reliable results concerning speeds rather than distances. Questionnaires analyses show that the simulator’s faith to reality regarding the display, sound effect and perspective is medium.